Papers with Code 아카이브
3D Object Super-Resolution
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3D object super-resolution is the task of up-sampling 3D objects.
<span style="color:grey; opacity: 0.6">( Image credit: Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/MKFMIKU/SDAN
공식 코드SDAN: Squared Deformable Alignment Network for Learning Misaligned Optical Zoom
https://github.com/MKFMIKU/SrSENet
공식 코드An Effective Single-Image Super-Resolution Model Using Squeeze-and-Excitation Networks
https://github.com/Maclory/Deep-Iterative-Collaboration
공식 코드https://github.com/Maclory/SPSR
공식 코드Structure-Preserving Super Resolution with Gradient Guidance
https://github.com/Madhu081096/Reference-based-MRI-superresolution-using-texture-transfer
공식 코드Reference-based Texture transfer for Single Image Super-resolution of Magnetic Resonance images
https://github.com/MandyZChen/srez
공식 코드https://github.com/ManuelFritsche/real-world-sr
공식 코드https://github.com/MclTTI/RainScaleGAN
공식 코드RainScaleGAN: a Conditional Generative Adversarial Network for Rainfall Downscaling
https://github.com/Mehran-Soltani/ChannelNet
공식 코드https://github.com/MengZephyr/Neural-Garment-Dynamic-Super-resolution
공식 코드https://github.com/MengyuanZha0/ICASSP24-NUV-DoA
공식 코드https://github.com/Microtreei/TSD-SR
공식 코드TSD-SR: One-Step Diffusion with Target Score Distillation for Real-World Image Super-Resolution
https://github.com/Mnster00/CasualSR
공식 코드CausalSR: Structural Causal Model-Driven Super-Resolution with Counterfactual Inference
https://github.com/MohammadChavosh/synthetic-fingerprint-generation
공식 코드https://github.com/Mukosame/Zooming-Slow-Mo-CVPR-2020
공식 코드Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution
https://github.com/ND-HowardGroup/SPIE-CNN-SR
공식 코드Deep learning-based super-resolution fluorescence microscopy on small datasets
https://github.com/NJU-Jet/SR_Mobile_Quantization
공식 코드https://github.com/NarcissusEx/CuNeRF
공식 코드https://github.com/Nicholasli1995/EgoNet
공식 코드Exploring intermediate representation for monocular vehicle pose estimation
https://github.com/OmarSZamzam/PixelShuffler
공식 코드PixelShuffler: A Simple Image Translation Through Pixel Rearrangement
https://github.com/PaddlePaddle/PaddleGAN
공식 코드https://github.com/Palantir-SR/palantir
공식 코드Palantir: Towards Efficient Super Resolution for Ultra-high-definition Live Streaming
https://github.com/Paper99/SRFBN_CVPR19
공식 코드https://github.com/Pengyu-gis/Historical-Aerial-Photos
공식 코드A GAN-Enhanced Deep Learning Framework for Rooftop Detection from Historical Aerial Imagery
https://github.com/PeterWang512/CNNDetection
공식 코드CNN-generated images are surprisingly easy to spot... for now
https://github.com/Plazzzzma/CP_project_2020
공식 코드https://github.com/REIYANG/GLORIA
공식 코드Hyperspectral Super-Resolution via Global-Local Low-Rank Matrix Estimation
https://github.com/REIYANG/HiBCD
공식 코드Hybrid Inexact BCD for Coupled Structured Matrix Factorization in Hyperspectral Super-Resolution
https://github.com/Rainyfish/FASRGAN-and-Fs-SRGAN
공식 코드https://github.com/RaoUmer/ISRResCNet
공식 코드Deep Iterative Residual Convolutional Network for Single Image Super-Resolution
https://github.com/RaoUmer/SRResCGAN
공식 코드Deep Generative Adversarial Residual Convolutional Networks for Real-World Super-Resolution
https://github.com/RaoUmer/SRResCycGAN
공식 코드Deep Cyclic Generative Adversarial Residual Convolutional Networks for Real Image Super-Resolution
https://github.com/SAIL-project/RBC-FNO-Surrogate
공식 코드Solving Turbulent Rayleigh-Bénard Convection using Fourier Neural Operators
https://github.com/SHI-Labs/Cross-Scale-Non-Local-Attention
공식 코드Image Super-Resolution with Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars Mining
https://github.com/SHI-Labs/Pyramid-Attention-Networks
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.